Project Summary/Abstract The vast majority of service-sector workers now report instability in their weekly work schedules. These hourly workers are given little advance notice of their schedules and experience substantial volatility in their schedules and hours worked from day-to-day and week-to-week. There are well-known relationships between health and other aspects of work such as non-standard schedules in low-wage work and inflexible employment arrangements. However, there is a gap in knowledge about how schedule instability and unpredictability affects health and well-being. One important reason for this gap is a lack of suitable data containing information on both work schedules and health outcomes. Yet, even in the absence of empirical research, policy makers and companies are beginning to change scheduling policies and practices. There is a critical need to collect new data that will allow researchers to estimate the health effects of unstable and unpredictable work schedules. Our overall hypothesis is that precarious work will have deleterious impacts on depression, sleep, nutrition, substance use, as well as parenting behaviors, and therefore, child health and well-being. First, the proposed research develops and deploys an innovative method for collecting survey data at scale, at low-cost, and with speed from a target population of service-sector workers. We use the Facebook advertising platform to purchase and place survey recruitment advertisements in the mobile and desktop newsfeeds of Facebook users who work at retail and fast food establishments. This approach allows us to target users with particular employers and/or in specific localities. We evaluate this method in terms of cost, data quality, and consistency with external benchmarks. Second, these data are drawn using non-probability sampling and we employ post-stratification and weighting to adjust for biases in the demographic characteristics of our sample. The proposed research also develops a new method for assessing non-demographic biases in response. We test alternative advertising messages that elicit survey responses on hypothesized confounding unobservable characteristics. We then estimate schedule effects separately by recruitment message to put bounds around our estimates. Third, the proposed research uses these data and methods to estimate the relationship between job scheduling practices and worker and family health. One set of analyses will estimate the associations between job characteristics and health outcomes. A second set of analyses will exploit city and company policy changes using rigorous difference-in-differences methodology to estimate causal effects of unstable and unpredictable work schedules. In sum, we develop an innovative data collection approach combined with rigorous estimation to take advantage of natural experiments implemented when work-scheduling laws or company practices change. The significant contributions of our project entail testing a versatile, low-cost, and rapid-response data collection approach and providing key missing evidence on health impacts of work schedule instability.